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Background: Moschus is a rare and precious natural medicine. Due to the properties of resources scarcity and expensive price of natural musk, artificial musk has been developed as substitute materials in some prescriptions. Rapid and accurate identification of natural or artificial musk in complex traditional Chinese medicine (TCM) preparations is also a challenge. Method: A strategy from non-targeted to targeted gas chromatography-mass spectrometry (GC–MS) metabolomics was developed for discrimination of natural and artificial musk. Firstly, GC–MS-based non-targeted analysis combined with chemometrics was used to find the potential chemical markers to distinguish natural musk and artificial musk. Subsequently, targeted metabolomics was used to analyze musk in preparations with multiple reaction monitoring (MRM) mode by use gas chromatography coupled with triple quadrupole mass spectrometry (GC-QQQ MS). Results: Two chemical markers named prasterone and androsterone have been selected and could be detected in all Compound Pien Tze Huang preparations (CPZHs) containing artificial musk, while the CPZHs containing natural musk did not detect two markers with S/N (signal to noise ratio) less than 3. Conclusion: Our work provides an applicable approach to select the practical chemical markers for the assessment of musk in preparations to realize the traceability of musk in TCM and improve the quality control of musk-containing preparations. [ABSTRACT FROM AUTHOR]

Rapid characterization of chemical analogues in potentially toxic complex matrix was essential for prevention of accidental poisoning. On the basis of the fragment ions possessed not only the same retention times (RT) but the same drift times (DT) on liquid chromatography-ion mobility-mass spectrometry (LC-IM-MS), an alternating frames (AF)-data independent acquisition (DIA) was utilized for simultaneous detection and rapid match of precursor/product ions with a fast switch of low/high collision energy (CE). A diagnostic ions guided 2D-locating strategy was developed for identification of chemical analogues in potentially toxic herbal medicines using LC-IM-MS. Firstly, the 2D-locations (RT, DT) of diagnostic ions were screened from high-fragment IM-MS frames according to their m/z. Then, the correlated precursor ions were extracted from the complex background interference in low-fragment IM-MS frames based on diagnostic ions' 2D-locations. Finally, the remained ions were characterized using double-bond equivalent analysis combined with MS/MS fragment interpretation. Totally, 236 diterpene alkaloids including eight compounds with potential new esterification types were characterized in processed lateral roots of Aconitum carmichaelii Debx. Moreover, the LC-IM-MS distribution regularities of diterpene alkaloids with various chemical structure types were further investigated and discussed. This study presented an innovative idea for revealing the chemical basis related to the toxicities of potentially poisonous herbal medicines to ensure the medication safety. [ABSTRACT FROM AUTHOR]

Trace analysis of chemical analogues was always a hot topic and attracted the researchers' attentions. In this study, a database/multi-step filtering strategy was developed by using HPLC-Chip-MS system for comprehensive characterization of monacolin analogues in monascus -fermented rice product (MFRP). This strategy mainly included three steps, including chemical profile of MFRP by HPLC-Chip-MS, establishment of monacolin analogue database, multi-step filtering of monacolins based on modified mass defect filtering. All target compounds showed the symmetrical peak shapes and high MS response by using nanoflow HPLC-Chip-MS. According to the previous literature and experimental MS data, a database including 720 monacolin analogues was established. The original 522 ions in MFRP were automatically extracted by molecular feature extraction function. And then, through rectangular mass defect filtering and analogues distribution area filtering, 298 ions were further excluded. Finally, a total of 84 monacolins including 16 new compounds were unambiguously identified or tentatively characterized based on MS/MS information. In comparison with conventional mass defect filtering method, the proposed method was accurately able to exclude false positive results. The 438 false positive results were excluded in our study, while only 250 ones would be filtered out by using conventional method. This study provided a sensitive and powerful method for rapidly characterization of trace chemicals in complex matrix. [ABSTRACT FROM AUTHOR]

Qi-Fu-Yin (QFY), a classical traditional Chinese medicine formula, is proven to have significant neuroprotective effects by modern pharmacological studies. However, the chemical constituents of QFY have not been fully explored. In this study, an ultra-high performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF MS) was developed for comprehensive analysis of chemical constituents in QFY. By using characteristic ions and fragmentation rules, a reliable identification of 156 compounds was described here, including 69 triterpene saponins, 23 oligosaccharide esters, 22 flavanoids, 9 alkaloids, 9 phenolic acids, 8 phthalides, 7 phenylethanoid glycosides, 3 xanthones, 3 sesquiterpene lactones, 2 ionones and 1 iridoid glycoside. Twenty-six major compounds were then determined in a single run by UHPLC coupled with triple quadrupole tandem mass spectrometry (QQQ MS) with fast positive/negative polarity switching. It allows for the acquisition of MS data in both ionization modes from a single run. The proposed method was then validated in terms of linearity, accuracy, precision and recovery. The overall recoveries for 26 analytes ranged from 91.35% to 109.58%, with RSDs ranging from 0.82% to 4.83%. In addition, the content of 26 analytes in QFY prepared by five batches of herbal materials was also analyzed. These results demonstrated that our present method was effective and reliable for comprehensive quality evaluation of QFY. Meanwhile, the study might provide the chemical evidence for revealing the material basis of its therapeutic effects. [ABSTRACT FROM AUTHOR]

Ion mobility coupled with mass spectrometry (IM-MS), an emerging technology for analysis of complex matrix, has been facing challenges due to the complexities of chemical structures and original data, as well as low-efficiency and error-proneness of manual operations. In this study, we developed a structural similarity networking assisted collision cross-section prediction interval filtering (SSN-CCSPIF) strategy. We first carried out a structural similarity networking (SSN) based on Tanimoto similarities among Morgan fingerprints to classify the authentic compounds potentially existing in complex matrix. By performing automatic regressive prediction statistics on mass-to-charge ratios (m/z) and collision cross-sections (CCS) with a self-built Python software, we explored the IM-MS feature trendlines, established filtering intervals and filtered potential compounds for each SSN classification. Chemical structures of all filtered compounds were further characterized by interpreting their multidimensional IM-MS data. To evaluate the applicability of SSN-CCSPIF, we selected Ginkgo biloba extract and dripping pills. The SSN-CCSPIF subtracted more background interferences (43.24%∼43.92%) than other similar strategies with conventional ClassyFire criteria (10.71%∼12.13%) or without compound classification (35.73%∼36.63%). Totally, 229 compounds, including eight potential new compounds, were characterized. Among them, seven isomeric pairs were discriminated with the integration of IM-separation. Using SSN-CCSPIF, we can achieve high-efficient analysis of complex IM-MS data and comprehensive chemical profiling of complex matrix to reveal their material basis. [Display omitted] • Multi-molecules were classified by fingerprints-based structural similarity networking (SSN). • A Python software was built for IM-MS feature filtering by CCS prediction interval (CCSPIF). • Each classification of potential compounds was rapidly filtered from IM-MS data of GBE and GBDP. • Totally, 229 compounds were identified by performing SSN-CCSPIF on UHPLC-IM-QTOF MS. • Eight potential new compounds without previous reports were tentatively characterized. [ABSTRACT FROM AUTHOR]